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Home>IIot & Smart Technology>Big Data>Picture this: Switching maintenance data to visual
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Home>IIot & Smart Technology>Connectivity>Picture this: Switching maintenance data to visual

Picture this: Switching maintenance data to visual

24 October 2022

Operating personnel at all levels are gaining revealing, and sometimes unexpected, insights into the health of machinery by viewing and interpreting visual representations of data from sensors, writes David Hannaby

DO YOU ever wish you had x-ray eyes to see inside a machine or crystal ball to predict exactly when it will fail? The reality is more likely to be a relentless routine of maintenance checklists and service inspections, together with time-wasting preventive procedures. There are still unexpected quality lapses and machine failures. Reactive maintenance visits are too frequent and additional machine stoppages are unavoidable.

Engineers are discovering that the ability to visualise sensor data in new and surprising ways transforms it into a powerful resource to better understand the health of machines, and to predict what will happen next. Whether that is a series of graphs on a dashboard, an overview of the machine or plant, or an Augmented Reality representation, the principle is: the simpler the better.

In Maintenance 4.0, data from sensors is enabled through networked connectivity and software, either locally on site or in the cloud. It gives operating staff at all levels the power not only to get health checks in real time, but to recognise trends and identify patterns.

The technology doesn’t have to be complex, time-consuming, intrusive or insecure. It can be incremental, low-risk and transformative. It could be as straightforward as managing a digital twin of all your assets along their entire life cycles. So, for example, our customers use the SICK AssetHub to see a feature-rich and interactive view of all sensors, systems and other devices: useful information that’s right at the fingertips of a maintenance operative from a smart phone.

In a completely different way, Augmented Reality offers an exciting, and surprisingly simple, visualisation of data from sensors. New developments in the technology are enabling sensor data to be merged with a camera picture and the results displayed on a smart phone.

SICK’s first development is SARA, the SICK Augmented Reality Assistant. SARA has enabled simple troubleshooting and configuration of LiDAR sensors on Automated Mobile Robots. Diagnosis and correction of machine downtime, such as a field infringement, can be done ‘on the spot’ without the need to connect a PC.

The vision is for SARA to work with any SICK sensor and with any data provider, technology and vendor independent.

Maintenance 4.0

Most people are familiar with the ability of Smart Sensors to output diagnostic data and provide additional information, either about their own status, e.g. “Does my screen need cleaning?” or their process performance: “How many times have I detected something?” Even this simple data can lead to more informed maintenance interventions. 

But, because sensors are often positioned right in the heart of machinery, they can also provide insights over and above their function. Take the SICK MPS-G position sensor, for example. It is used to detect the position of the piston in small cylinders. However, it also provides comprehensive diagnostic data via IO-Link on the piston velocity, cylinder stroke, magnetic fields strengths, temperature, vibration, and acceleration. These values can help to track the performance of a pneumatic drive, as well as the service status of the machinery.

SICK has also developed a condition monitoring sensor for servo motors. When added as an extension to a SICK EDS/EDM25 motor feedback encoder, the sHub provides temperature, vibration, position and speed data. So critical mechanical failures, such as ball bearing damage or motor imbalance, can be detected early to pre-empt machinery downtime.

Real-time condition monitoring

The recent launch of SICK’s MPB Multi-Physics Box Condition Monitoring Sensor offers an opportunity, quite literally, to bolt on real-time, continuous condition monitoring to many different machines, including motors, pumps, conveyor systems or fans.

The SICK MPB measures vibration, shocks and temperature. It can be set up to alert when measured values exceed pre-configured thresholds. By considering previously disparate sets of data together, new insights are gained. As a result, changes in performance are detected early and maintenance work can be planned based on real data.

Getting visibility to the data from your machines is just the first step to taking proactive, rather than reactive, service and maintenance decisions. You also need the connectivity, e.g. via an IoT gateway device, to deliver the data securely. Most importantly, you need the ability to integrate, visualise and analyse the data exactly where and when you need it. SICK’s IntegrationSpace is our distribution channel for a modular portfolio of digital tools, services and cloud-based applications that enable users to do this.

Monitoring box

As part of these services, the SICK Monitoring Box facilitates digital integration and visualisation for SICK customers. The Monitoring Box is not actually a physical box, rather an important digital services platform that enables plug-and-play condition monitoring to assist with preventative and predictive maintenance of sensors, machines, processes and plants. It can be adapted for all sorts of operating requirements to provide live status feedback and historical analysis supporting more effective maintenance and optimised efficiency.

When enabled using pre-configured Apps running on SICK smart sensors, the Monitoring Box provides transparent data monitoring through an intuitive, browser-based dashboard for desktop or mobile devices.

Configure the SICK Monitoring Box, and transparent information about the health of your machines is just a few short steps away. Depending on your requirements, information such as operating hours, wear, temperature, energy usage or level of contamination, is turned into a valuable resource.

Crucially, the Monitoring Box also affords users the power to predict e.g. to help to calculate based on real measurement values when a particular component or device is nearing the point of failure, so that it can be replaced before it leads to down time.

Maintenance programs to keep your devices and systems in good condition can be inferred based on diagnoses, statistics and predictions. This makes it possible to carry out inspections, repairs and maintenance in a quick and tailored way, and to plan servicing more reliably.

We are already seeing how early adopters are gaining unexpected insights. For example, using SICK’s monitoring app for its FTMg multifunctional flow sensor, our customer was able to identify energy cost savings from compressed air usage. By tracking consumption over time, compressed air energy losses were also easier to spot and correct. The visualised data made it easy for the production team to identify ways of making start-up and shutdown processes more energy efficient, improving compressor control and managing peak loads.

When times are hard, the temptation is to stick with what you know. Yet the received wisdom is to get on board with Industry 4.0 digital technologies in ways that are disruptive and transformative. Seeing Maintenance 4.0 through the eyes of sensors offers a way to reconcile these apparently conflicting pressures. So, you really can squeeze every last drop out of your legacy assets while embracing new digital technologies. The results can be surprising and truly transformative.

David Hannaby is market product manager at SICK UK


Key Points

  • Engineers are discovering that the ability to visualise sensor data in new and surprising ways transforms it into a powerful resource
  • New developments in the AR are enabling sensor data to be merged with a camera picture and the results displayed on a smart phone
  • Getting visibility to the data from machines is just the first step to taking proactive, rather than reactive, service and maintenance decisions